package com.media.system.service.impl;

import com.media.system.domain.MediaFile;
import com.media.system.mapper.MediaFileMapper;
import com.media.system.service.AiTagService;
import com.media.system.service.dto.AiTagResult;
import lombok.extern.slf4j.Slf4j;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.scheduling.annotation.Async;
import org.springframework.stereotype.Service;

import java.math.BigDecimal;
import java.util.*;
import java.util.concurrent.CompletableFuture;
import java.util.stream.Collectors;

/**
 * AI标签识别服务实现
 * 
 * @author Claude
 * @since 2024-01-20
 */
@Service
@Slf4j
public class AiTagServiceImpl implements AiTagService {

    @Autowired
    private MediaFileMapper mediaFileMapper;

    // 模拟AI服务配置
    private static final Map<String, List<String>> CATEGORY_TAGS = new HashMap<String, List<String>>() {{
        put("人物", Arrays.asList("人物", "肖像", "家庭", "朋友", "孩子", "老人", "年轻人"));
        put("风景", Arrays.asList("山", "海", "湖", "森林", "城市", "乡村", "日出", "日落", "天空"));
        put("动物", Arrays.asList("狗", "猫", "鸟", "马", "牛", "羊", "野生动物", "宠物"));
        put("植物", Arrays.asList("花", "树", "草", "叶子", "森林", "花园", "盆栽"));
        put("食物", Arrays.asList("美食", "水果", "蔬菜", "甜点", "饮料", "中餐", "西餐", "烘焙"));
        put("建筑", Arrays.asList("房屋", "桥梁", "教堂", "寺庙", "摩天大楼", "古建筑", "现代建筑"));
        put("交通", Arrays.asList("汽车", "自行车", "火车", "飞机", "船", "摩托车", "公交车"));
        put("活动", Arrays.asList("旅行", "聚会", "运动", "工作", "学习", "娱乐", "庆祝", "婚礼"));
        put("物品", Arrays.asList("书籍", "玩具", "工具", "电子产品", "服装", "家具", "装饰品"));
        put("季节", Arrays.asList("春天", "夏天", "秋天", "冬天", "雪", "雨", "阳光", "云朵"));
        put("情感", Arrays.asList("快乐", "悲伤", "惊讶", "愤怒", "恐惧", "平静", "兴奋", "温馨"));
        put("颜色", Arrays.asList("红色", "蓝色", "绿色", "黄色", "紫色", "橙色", "黑色", "白色", "粉色"));
    }};

    @Override
    public AiTagResult recognizeImageTags(MediaFile mediaFile) {
        log.info("开始识别图片标签: {}", mediaFile.getFileName());
        
        long startTime = System.currentTimeMillis();
        
        try {
            // 模拟AI识别过程
            Thread.sleep(1000 + new Random().nextInt(2000)); // 1-3秒处理时间
            
            // 生成模拟的识别结果
            List<AiTagResult.AiTag> tags = generateMockImageTags();
            List<AiTagResult.DetectedObject> objects = generateMockObjects();
            List<AiTagResult.AiTag> scenes = generateMockScenes();
            List<AiTagResult.DetectedFace> faces = generateMockFaces();
            List<AiTagResult.ColorInfo> colors = generateMockColors();
            
            long processingTime = System.currentTimeMillis() - startTime;
            
            return AiTagResult.builder()
                    .fileId(mediaFile.getFileId())
                    .status("success")
                    .tags(tags)
                    .objects(objects)
                    .scenes(scenes)
                    .faces(faces)
                    .dominantColors(colors)
                    .qualityScore(BigDecimal.valueOf(0.85 + Math.random() * 0.15))
                    .safetyScore(BigDecimal.valueOf(0.95 + Math.random() * 0.05))
                    .recommendedAlbums(generateRecommendedAlbums(tags))
                    .processingTime(processingTime)
                    .build();
                    
        } catch (Exception e) {
            log.error("图片标签识别失败: {}", e.getMessage(), e);
            return AiTagResult.builder()
                    .fileId(mediaFile.getFileId())
                    .status("failed")
                    .errorMessage(e.getMessage())
                    .processingTime(System.currentTimeMillis() - startTime)
                    .build();
        }
    }

    @Override
    public AiTagResult recognizeVideoTags(MediaFile mediaFile) {
        log.info("开始识别视频标签: {}", mediaFile.getFileName());
        
        long startTime = System.currentTimeMillis();
        
        try {
            // 模拟视频AI识别过程（更耗时）
            Thread.sleep(3000 + new Random().nextInt(5000)); // 3-8秒处理时间
            
            List<AiTagResult.AiTag> tags = generateMockVideoTags();
            List<AiTagResult.AiTag> scenes = generateMockScenes();
            
            long processingTime = System.currentTimeMillis() - startTime;
            
            return AiTagResult.builder()
                    .fileId(mediaFile.getFileId())
                    .status("success")
                    .tags(tags)
                    .scenes(scenes)
                    .qualityScore(BigDecimal.valueOf(0.80 + Math.random() * 0.20))
                    .safetyScore(BigDecimal.valueOf(0.90 + Math.random() * 0.10))
                    .recommendedAlbums(generateRecommendedAlbums(tags))
                    .processingTime(processingTime)
                    .metadata(Map.of(
                        "duration", mediaFile.getDuration() != null ? mediaFile.getDuration() : 0,
                        "hasAudio", true,
                        "frameRate", 30
                    ))
                    .build();
                    
        } catch (Exception e) {
            log.error("视频标签识别失败: {}", e.getMessage(), e);
            return AiTagResult.builder()
                    .fileId(mediaFile.getFileId())
                    .status("failed")
                    .errorMessage(e.getMessage())
                    .processingTime(System.currentTimeMillis() - startTime)
                    .build();
        }
    }

    @Override
    public AiTagResult recognizeAudioTags(MediaFile mediaFile) {
        log.info("开始识别音频标签: {}", mediaFile.getFileName());
        
        long startTime = System.currentTimeMillis();
        
        try {
            // 模拟音频AI识别过程
            Thread.sleep(2000 + new Random().nextInt(3000)); // 2-5秒处理时间
            
            List<AiTagResult.AiTag> tags = generateMockAudioTags();
            
            long processingTime = System.currentTimeMillis() - startTime;
            
            return AiTagResult.builder()
                    .fileId(mediaFile.getFileId())
                    .status("success")
                    .tags(tags)
                    .qualityScore(BigDecimal.valueOf(0.75 + Math.random() * 0.25))
                    .safetyScore(BigDecimal.valueOf(0.98 + Math.random() * 0.02))
                    .recommendedAlbums(Arrays.asList("音乐收藏", "语音记录", "音频文件"))
                    .processingTime(processingTime)
                    .metadata(Map.of(
                        "duration", mediaFile.getDuration() != null ? mediaFile.getDuration() : 0,
                        "sampleRate", 44100,
                        "bitRate", 320
                    ))
                    .build();
                    
        } catch (Exception e) {
            log.error("音频标签识别失败: {}", e.getMessage(), e);
            return AiTagResult.builder()
                    .fileId(mediaFile.getFileId())
                    .status("failed")
                    .errorMessage(e.getMessage())
                    .processingTime(System.currentTimeMillis() - startTime)
                    .build();
        }
    }

    @Override
    @Async
    public List<AiTagResult> batchRecognizeTags(List<MediaFile> mediaFiles) {
        log.info("开始批量处理AI标签识别，文件数量: {}", mediaFiles.size());
        
        List<CompletableFuture<AiTagResult>> futures = mediaFiles.stream()
                .map(mediaFile -> CompletableFuture.supplyAsync(() -> {
                    switch (mediaFile.getFileType().toLowerCase()) {
                        case "image":
                            return recognizeImageTags(mediaFile);
                        case "video":
                            return recognizeVideoTags(mediaFile);
                        case "audio":
                            return recognizeAudioTags(mediaFile);
                        default:
                            return AiTagResult.builder()
                                    .fileId(mediaFile.getFileId())
                                    .status("skipped")
                                    .errorMessage("不支持的文件类型: " + mediaFile.getFileType())
                                    .build();
                    }
                }))
                .collect(Collectors.toList());

        return futures.stream()
                .map(CompletableFuture::join)
                .collect(Collectors.toList());
    }

    @Override
    public List<String> getRecommendedTags(Long userId, String fileType) {
        // 基于用户历史标签使用情况推荐
        List<String> recommendedTags = new ArrayList<>();
        
        switch (fileType.toLowerCase()) {
            case "image":
                recommendedTags.addAll(Arrays.asList("家庭", "旅行", "美食", "风景", "人物", "宠物"));
                break;
            case "video":
                recommendedTags.addAll(Arrays.asList("生活记录", "旅行视频", "家庭时光", "庆祝", "运动"));
                break;
            case "audio":
                recommendedTags.addAll(Arrays.asList("音乐", "语音备忘", "通话录音", "学习资料"));
                break;
            default:
                recommendedTags.addAll(Arrays.asList("文档", "工作", "学习", "备份"));
        }
        
        return recommendedTags;
    }

    @Override
    public List<String> getPopularTags(Integer limit) {
        List<String> popularTags = Arrays.asList(
            "家庭", "旅行", "美食", "风景", "人物", "宠物", "生活",
            "庆祝", "工作", "学习", "运动", "音乐", "朋友", "孩子",
            "节日", "生日", "婚礼", "聚会", "户外", "城市"
        );
        
        Collections.shuffle(popularTags);
        return popularTags.stream().limit(limit != null ? limit : 10).collect(Collectors.toList());
    }

    @Override
    public List<String> smartClassifyToAlbums(List<MediaFile> mediaFiles) {
        Map<String, Integer> albumCategories = new HashMap<>();
        
        for (MediaFile mediaFile : mediaFiles) {
            // 基于AI标签智能分类到相册
            if (mediaFile.getAiTags() != null) {
                String[] tags = mediaFile.getAiTags().split(",");
                for (String tag : tags) {
                    String album = mapTagToAlbum(tag.trim());
                    albumCategories.merge(album, 1, Integer::sum);
                }
            }
        }
        
        return albumCategories.entrySet().stream()
                .sorted(Map.Entry.<String, Integer>comparingByValue().reversed())
                .map(Map.Entry::getKey)
                .limit(5)
                .collect(Collectors.toList());
    }

    @Override
    public List<MediaFile> detectSimilarFiles(MediaFile mediaFile) {
        // 模拟相似文件检测
        // 实际实现中会使用图像hash算法、特征向量比较等
        return new ArrayList<>();
    }

    @Override
    public boolean contentSafetyCheck(MediaFile mediaFile) {
        // 模拟内容安全检测
        // 实际实现中会调用内容审核API
        return true; // 默认认为安全
    }

    // 以下是模拟数据生成方法

    private List<AiTagResult.AiTag> generateMockImageTags() {
        List<String> imageTags = Arrays.asList("人物", "风景", "建筑", "动物", "植物", "食物", "活动", "物品");
        return imageTags.stream()
                .limit(3 + new Random().nextInt(3))
                .map(tag -> AiTagResult.AiTag.builder()
                        .name(tag)
                        .confidence(BigDecimal.valueOf(0.7 + Math.random() * 0.3))
                        .category("通用")
                        .build())
                .collect(Collectors.toList());
    }

    private List<AiTagResult.AiTag> generateMockVideoTags() {
        List<String> videoTags = Arrays.asList("生活记录", "旅行", "庆祝", "运动", "表演", "教学", "新闻", "娱乐");
        return videoTags.stream()
                .limit(2 + new Random().nextInt(3))
                .map(tag -> AiTagResult.AiTag.builder()
                        .name(tag)
                        .confidence(BigDecimal.valueOf(0.6 + Math.random() * 0.4))
                        .category("视频")
                        .build())
                .collect(Collectors.toList());
    }

    private List<AiTagResult.AiTag> generateMockAudioTags() {
        List<String> audioTags = Arrays.asList("音乐", "语音", "通话", "自然声音", "乐器", "歌唱", "演讲", "录音");
        return audioTags.stream()
                .limit(2)
                .map(tag -> AiTagResult.AiTag.builder()
                        .name(tag)
                        .confidence(BigDecimal.valueOf(0.8 + Math.random() * 0.2))
                        .category("音频")
                        .build())
                .collect(Collectors.toList());
    }

    private List<AiTagResult.DetectedObject> generateMockObjects() {
        List<String> objects = Arrays.asList("人", "汽车", "树", "建筑", "动物", "食物");
        return objects.stream()
                .limit(1 + new Random().nextInt(2))
                .map(obj -> AiTagResult.DetectedObject.builder()
                        .name(obj)
                        .confidence(BigDecimal.valueOf(0.8 + Math.random() * 0.2))
                        .boundingBox(AiTagResult.BoundingBox.builder()
                                .x(new Random().nextInt(100))
                                .y(new Random().nextInt(100))
                                .width(50 + new Random().nextInt(100))
                                .height(50 + new Random().nextInt(100))
                                .build())
                        .build())
                .collect(Collectors.toList());
    }

    private List<AiTagResult.AiTag> generateMockScenes() {
        List<String> scenes = Arrays.asList("室内", "户外", "城市", "乡村", "海边", "山区", "办公室", "家庭");
        return scenes.stream()
                .limit(1)
                .map(scene -> AiTagResult.AiTag.builder()
                        .name(scene)
                        .confidence(BigDecimal.valueOf(0.75 + Math.random() * 0.25))
                        .category("场景")
                        .build())
                .collect(Collectors.toList());
    }

    private List<AiTagResult.DetectedFace> generateMockFaces() {
        Random random = new Random();
        if (random.nextBoolean()) {
            return Arrays.asList(AiTagResult.DetectedFace.builder()
                    .faceId("face_" + random.nextInt(10000))
                    .estimatedAge(20 + random.nextInt(40))
                    .gender(random.nextBoolean() ? "男" : "女")
                    .emotion(Arrays.asList("快乐", "平静", "惊讶", "悲伤").get(random.nextInt(4)))
                    .confidence(BigDecimal.valueOf(0.85 + Math.random() * 0.15))
                    .boundingBox(AiTagResult.BoundingBox.builder()
                            .x(random.nextInt(200))
                            .y(random.nextInt(200))
                            .width(80 + random.nextInt(40))
                            .height(80 + random.nextInt(40))
                            .build())
                    .build());
        }
        return new ArrayList<>();
    }

    private List<AiTagResult.ColorInfo> generateMockColors() {
        String[] colors = {"#FF6B6B", "#4ECDC4", "#45B7D1", "#96CEB4", "#FFEAA7", "#DDA0DD"};
        String[] colorNames = {"红色", "青色", "蓝色", "绿色", "黄色", "紫色"};
        
        List<AiTagResult.ColorInfo> colorInfos = new ArrayList<>();
        for (int i = 0; i < 3; i++) {
            int index = new Random().nextInt(colors.length);
            colorInfos.add(AiTagResult.ColorInfo.builder()
                    .hex(colors[index])
                    .rgb(hexToRgb(colors[index]))
                    .percentage(BigDecimal.valueOf(20 + Math.random() * 40))
                    .colorName(colorNames[index])
                    .build());
        }
        return colorInfos;
    }

    private List<String> generateRecommendedAlbums(List<AiTagResult.AiTag> tags) {
        Set<String> albums = new HashSet<>();
        for (AiTagResult.AiTag tag : tags) {
            albums.add(mapTagToAlbum(tag.getName()));
        }
        return new ArrayList<>(albums);
    }

    private String mapTagToAlbum(String tag) {
        if (Arrays.asList("人物", "家庭", "朋友", "孩子").contains(tag)) {
            return "家庭相册";
        } else if (Arrays.asList("旅行", "风景", "城市", "海边").contains(tag)) {
            return "旅行回忆";
        } else if (Arrays.asList("美食", "食物", "餐厅").contains(tag)) {
            return "美食记录";
        } else if (Arrays.asList("宠物", "动物", "狗", "猫").contains(tag)) {
            return "萌宠时光";
        } else if (Arrays.asList("庆祝", "节日", "生日", "婚礼").contains(tag)) {
            return "特殊时刻";
        }
        return "生活记录";
    }

    private int[] hexToRgb(String hex) {
        hex = hex.replace("#", "");
        int r = Integer.parseInt(hex.substring(0, 2), 16);
        int g = Integer.parseInt(hex.substring(2, 4), 16);
        int b = Integer.parseInt(hex.substring(4, 6), 16);
        return new int[]{r, g, b};
    }
}